This invention relates generally to the field of detecting excessively hot rail car surfaces and more specifically to the use of rank filters to process infrared signals emitted by rail car surfaces.
While the present disclosure emphasizes application of the present invention to detection of hot rail car wheel bearings, it will be obvious to one of ordinary skill in the art that the present invention is equally applicable to the detection of other hot rail car surfaces such as, by way of example but not limitation, rail car wheels.
Malfunctioning rail car wheel bearings radiate heat due to friction. To detect such overheated bearings, in an attempt to warn the operator and stop the train prior to complete bearing failure and potential train derailment, railroads have developed and deployed wayside hot bearing detectors (HBDs). Typical HBDs utilize pyroelectric infrared sensors for detecting heat profiles of the rail car wheel bearings as the rail cars roll past the sensor. As well as being pyroelectric, however, these sensor devices may often also be piezoelectric; that is, electrical outputs produced by these devices depend not only on the heat sensed, but also on sensed sound and vibration. The electrical noise pulses induced by undesirable piezoelectric effects are known as “microphonic artifacts”.
In some instances, microphonic artifacts present magnitudes similar to those of hot bearings. As conventional HBDs rely mainly on signal magnitudes for detection, microphonics and other phenomena can induce false alarms that result in stopping a train unnecessarily. Such false stops cost the railroad significant time and money.
While the signal magnitudes of microphonic artifacts are comparable to the signal magnitudes produced by truly hot bearings, the microphonic artifacts tend to present themselves as substantially sharper “spikes.” An opportunity exists, therefore, to reduce HBD sensitivity to microphonic artifacts through improved signal processing.